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Joo Y, Namgung E, Jeong H, Kang I, Kim J, Oh S, Lyoo IK, Yoon S, Hwang J. Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms. Sci Rep 2023; 13:22388. [PMID: 38104173 PMCID: PMC10725434 DOI: 10.1038/s41598-023-49514-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer's disease groups by identifying variances in brain age gaps between them, highlighting the algorithm's potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.
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Affiliation(s)
- Yoonji Joo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Eun Namgung
- Asan Institute for Life Sciences, Asan Medical Center, Seoul, South Korea
| | - Hyeonseok Jeong
- Department of Radiology, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Ilhyang Kang
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Jinsol Kim
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
| | - Sohyun Oh
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
| | - In Kyoon Lyoo
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea
- Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, South Korea
| | - Sujung Yoon
- Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.
- Department of Brain and Cognitive Sciences, Ewha Womans University, Seoul, South Korea.
| | - Jaeuk Hwang
- Department of Psychiatry, Soonchunhyang University College of Medicine, Seoul, South Korea.
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Avesta A, Hui Y, Aboian M, Duncan J, Krumholz HM, Aneja S. 3D Capsule Networks for Brain Image Segmentation. AJNR Am J Neuroradiol 2023; 44:562-568. [PMID: 37080721 PMCID: PMC10171390 DOI: 10.3174/ajnr.a7845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 03/11/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND AND PURPOSE Current autosegmentation models such as UNets and nnUNets have limitations, including the inability to segment images that are not represented during training and lack of computational efficiency. 3D capsule networks have the potential to address these limitations. MATERIALS AND METHODS We used 3430 brain MRIs, acquired in a multi-institutional study, to train and validate our models. We compared our capsule network with standard alternatives, UNets and nnUNets, on the basis of segmentation efficacy (Dice scores), segmentation performance when the image is not well-represented in the training data, performance when the training data are limited, and computational efficiency including required memory and computational speed. RESULTS The capsule network segmented the third ventricle, thalamus, and hippocampus with Dice scores of 95%, 94%, and 92%, respectively, which were within 1% of the Dice scores of UNets and nnUNets. The capsule network significantly outperformed UNets in segmenting images that were not well-represented in the training data, with Dice scores 30% higher. The computational memory required for the capsule network is less than one-tenth of the memory required for UNets or nnUNets. The capsule network is also >25% faster to train compared with UNet and nnUNet. CONCLUSIONS We developed and validated a capsule network that is effective in segmenting brain images, can segment images that are not well-represented in the training data, and is computationally efficient compared with alternatives.
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Affiliation(s)
- A Avesta
- From the Department of Radiology and Biomedical Imaging (A.A., M.A., J.D.)
- Department of Therapeutic Radiology (A.A., Y.H., S.A.)
- Center for Outcomes Research and Evaluation (A.A., Y.H., H.M.K., S.A.)
| | - Y Hui
- Department of Therapeutic Radiology (A.A., Y.H., S.A.)
- Center for Outcomes Research and Evaluation (A.A., Y.H., H.M.K., S.A.)
| | - M Aboian
- From the Department of Radiology and Biomedical Imaging (A.A., M.A., J.D.)
| | - J Duncan
- From the Department of Radiology and Biomedical Imaging (A.A., M.A., J.D.)
- Departments of Statistics and Data Science (J.D.)
- Biomedical Engineering (J.D., S.A.), Yale University, New Haven, Connecticut
| | - H M Krumholz
- Center for Outcomes Research and Evaluation (A.A., Y.H., H.M.K., S.A.)
- Division of Cardiovascular Medicine (H.M.K.), Yale School of Medicine, New Haven, Connecticut
| | - S Aneja
- Department of Therapeutic Radiology (A.A., Y.H., S.A.)
- Center for Outcomes Research and Evaluation (A.A., Y.H., H.M.K., S.A.)
- Biomedical Engineering (J.D., S.A.), Yale University, New Haven, Connecticut
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Avesta A, Hossain S, Lin M, Aboian M, Krumholz HM, Aneja S. Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation. Bioengineering (Basel) 2023; 10:181. [PMID: 36829675 PMCID: PMC9952534 DOI: 10.3390/bioengineering10020181] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/09/2023] [Accepted: 01/09/2023] [Indexed: 02/04/2023] Open
Abstract
Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models.
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Affiliation(s)
- Arman Avesta
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT 06510, USA
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, CT 06510, USA
| | - Sajid Hossain
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT 06510, USA
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, CT 06510, USA
| | - MingDe Lin
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
- Visage Imaging, Inc., San Diego, CA 92130, USA
| | - Mariam Aboian
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06510, USA
| | - Harlan M. Krumholz
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, CT 06510, USA
- Division of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT 06510, USA
| | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT 06510, USA
- Center for Outcomes Research and Evaluation, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06510, USA
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Sahayam S, Nenavath R, Jayaraman U, Prakash S. Brain tumor segmentation using a hybrid multi resolution U-Net with residual dual attention and deep supervision on MR images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Madsen MAJ, Wiggermann V, Marques MFM, Lundell H, Cerri S, Puonti O, Blinkenberg M, Christensen JR, Sellebjerg F, Siebner HR. Linking lesions in sensorimotor cortex to contralateral hand function in multiple sclerosis: a 7 T MRI study. Brain 2022; 145:3522-3535. [PMID: 35653498 PMCID: PMC9586550 DOI: 10.1093/brain/awac203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/17/2022] [Accepted: 05/20/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Cortical lesions constitute a key manifestation of multiple sclerosis and contribute to clinical disability and cognitive impairment. Yet it is unknown whether local cortical lesions and cortical lesion subtypes contribute to domain-specific impairments attributable to the function of the lesioned cortex.
In this cross-sectional study, we assessed how cortical lesions in the primary sensorimotor hand area (SM1-HAND) relate to corticomotor physiology and sensorimotor function of the contralateral hand. 50 relapse-free patients with relapsing-remitting or secondary-progressive multiple sclerosis and 28 healthy age- and sex-matched participants underwent whole-brain 7 T MRI to map cortical lesions. Brain scans were also used to estimate normalized brain volume, pericentral cortical thickness, white matter lesion fraction of the corticospinal tract, infratentorial lesion volume and the cross-sectional area of the upper cervical spinal cord. We tested sensorimotor hand function and calculated a motor and sensory composite score for each hand. In 37 patients and 20 healthy controls, we measured maximal motor evoked potential (MEP) amplitude, resting motor threshold and corticomotor conduction time with transcranial magnetic stimulation (TMS) and the N20 latency from somatosensory evoked potentials (SSEPs).
Patients showed at least one cortical lesion in the SM1-HAND in 47 of 100 hemispheres. The presence of a lesion was associated with worse contralateral sensory (P = 0.014) and motor (P = 0.009) composite scores. TMS of a lesion-positive SM1-HAND revealed a decreased maximal MEP amplitude (P < 0.001) and delayed corticomotor conduction (P = 0.002) relative to a lesion-negative SM1-HAND. Stepwise mixed linear regressions showed that the presence of an SM1-HAND lesion, higher white-matter lesion fraction of the corticospinal tract, reduced spinal cord cross-sectional area and higher infratentorial lesion volume were associated with reduced contralateral motor hand function. Cortical lesions in SM1-HAND, spinal cord cross-sectional area and normalized brain volume were also associated with smaller maximal MEP amplitude and longer corticomotor conduction times. The effect of cortical lesions on sensory function was no longer significant when controlling for MRI-based covariates. Lastly, we found that intracortical and subpial lesions had the largest effect on reduced motor hand function, intracortical lesions on reduced MEP amplitude and leukocortical lesions on delayed corticomotor conduction.
Together, this comprehensive multi-level assessment of sensorimotor brain damage shows that the presence of a cortical lesion in SM1-HAND is associated with impaired corticomotor function of the hand, after accounting for damage at the subcortical level. The results also provide preliminary evidence that cortical lesion types may affect the various facets of corticomotor function differentially.
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Affiliation(s)
- Mads A. J. Madsen
- Copenhagen University Hospital - Amager & Hvidovre Danish Research Centre for Magnetic Resonance, , 2650 Hvidovre, Denmark
| | - Vanessa Wiggermann
- Copenhagen University Hospital - Amager & Hvidovre Danish Research Centre for Magnetic Resonance, , 2650 Hvidovre, Denmark
| | - Marta F. M. Marques
- Copenhagen University Hospital - Amager & Hvidovre Danish Research Centre for Magnetic Resonance, , 2650 Hvidovre, Denmark
| | - Henrik Lundell
- Copenhagen University Hospital - Amager & Hvidovre Danish Research Centre for Magnetic Resonance, , 2650 Hvidovre, Denmark
| | - Stefano Cerri
- Copenhagen University Hospital - Amager & Hvidovre Danish Research Centre for Magnetic Resonance, , 2650 Hvidovre, Denmark
- Technical University of Denmark Department of Health Technology, , 2800 Kgs. Lyngby, Denmark
| | - Oula Puonti
- Copenhagen University Hospital - Amager & Hvidovre Danish Research Centre for Magnetic Resonance, , 2650 Hvidovre, Denmark
| | - Morten Blinkenberg
- Copenhagen University Hospital – Rigshospitalet Danish Multiple Sclerosis Center, Department of Neurology, , 2600 Glostrup, Denmark
| | - Jeppe Romme Christensen
- Copenhagen University Hospital – Rigshospitalet Danish Multiple Sclerosis Center, Department of Neurology, , 2600 Glostrup, Denmark
| | - Finn Sellebjerg
- Copenhagen University Hospital – Rigshospitalet Danish Multiple Sclerosis Center, Department of Neurology, , 2600 Glostrup, Denmark
- University of Copenhagen Department of Clinical Medicine, , 2200 Copenhagen, Denmark
| | - Hartwig R. Siebner
- Copenhagen University Hospital - Amager & Hvidovre Danish Research Centre for Magnetic Resonance, , 2650 Hvidovre, Denmark
- Copenhagen University Hospital - Bispebjerg & Frederiksberg Department of Neurology, , 2400 Copenhagen, Denmark
- University of Copenhagen Department of Clinical Medicine, , 2200 Copenhagen, Denmark
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Kwok WE. Basic Principles of and Practical Guide to Clinical MRI Radiofrequency Coils. Radiographics 2022; 42:898-918. [PMID: 35394887 DOI: 10.1148/rg.210110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Radiofrequency (RF) coils are an essential MRI component used for transmission of the RF field to excite nuclear spins and for reception of the MRI signal. They play an important role in image quality in terms of signal-to-noise ratio, signal uniformity, and image resolution. However, they are also associated with potential image artifacts and RF heating that may lead to patient burns. Knowledge of the basic principles of RF coils-including coil designs commonly used in clinical MRI and the anatomy of RF receive coils-facilitates understanding of the use and safety issues of RF coils. Selection of suitable RF coils for individual applications and proper use of RF coils in particular MRI techniques such as parallel imaging are needed to achieve optimal image quality, prevent image artifacts, and reduce the risk of RF burns. The ability to correctly identify RF coil problems and distinguish them from other problems with image artifacts resembling those of RF coil problems allows effective handling of the problems and efficient clinical MRI operation. Quality control of RF coils is required to ensure consistent image quality for clinical MRI and avoid coil problems that may affect image diagnostic evaluation or interrupt patient imaging. There are different phantom test methods for RF coil quality control; the appropriate one to use depends on the coil design and MRI system. An invited commentary by Ohliger is available online. Online supplemental material is available for this article. ©RSNA, 2022.
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Affiliation(s)
- Wingchi E Kwok
- From the Department of Imaging Sciences, University of Rochester, 601 Elmwood Ave, Rochester, NY 14642; and University of Rochester Center for Advanced Brain Imaging and Neurophysiology, Rochester, NY
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Zappalá S, Bennion NJ, Potts MR, Wu J, Kusmia S, Jones DK, Evans SL, Marshall D. Full-field MRI measurements of in-vivo positional brain shift reveal the significance of intra-cranial geometry and head orientation for stereotactic surgery. Sci Rep 2021; 11:17684. [PMID: 34480073 PMCID: PMC8417262 DOI: 10.1038/s41598-021-97150-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/13/2021] [Indexed: 11/15/2022] Open
Abstract
Positional brain shift (PBS), the sagging of the brain under the effect of gravity, is comparable in magnitude to the margin of error for the success of stereotactic interventions ([Formula: see text] 1 mm). This non-uniform shift due to slight differences in head orientation can lead to a significant discrepancy between the planned and the actual location of surgical targets. Accurate in-vivo measurements of this complex deformation are critical for the design and validation of an appropriate compensation to integrate into neuronavigational systems. PBS arising from prone-to-supine change of head orientation was measured with magnetic resonance imaging on 11 young adults. The full-field displacement was extracted on a voxel-basis via digital volume correlation and analysed in a standard reference space. Results showed the need for target-specific correction of surgical targets, as a significant displacement ranging from 0.52 to 0.77 mm was measured at surgically relevant structures. Strain analysis further revealed local variability in compressibility: anterior regions showed expansion (both volume and shape change), whereas posterior regions showed small compression, mostly dominated by shape change. Finally, analysis of correlation demonstrated the potential for further patient- and intervention-specific adjustments, as intra-cranial breadth and head tilt correlated with PBS reaching statistical significance.
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Affiliation(s)
- Stefano Zappalá
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK.
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
| | | | | | - Jing Wu
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
| | - Slawomir Kusmia
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Centre for Medical Image Computing, University College London, London, UK
- MRI Unit, Epilepsy Society, Chalfont St Peter, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Sam L Evans
- School of Engineering, Cardiff University, Cardiff, UK
| | - David Marshall
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
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Dai X, Lei Y, Liu Y, Wang T, Ren L, Curran WJ, Patel P, Liu T, Yang X. Intensity non-uniformity correction in MR imaging using residual cycle generative adversarial network. Phys Med Biol 2020; 65:215025. [PMID: 33245059 PMCID: PMC7934018 DOI: 10.1088/1361-6560/abb31f] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Correcting or reducing the effects of voxel intensity non-uniformity (INU) within a given tissue type is a crucial issue for quantitative magnetic resonance (MR) image analysis in daily clinical practice. Although having no severe impact on visual diagnosis, the INU can highly degrade the performance of automatic quantitative analysis such as segmentation, registration, feature extraction and radiomics. In this study, we present an advanced deep learning based INU correction algorithm called residual cycle generative adversarial network (res-cycle GAN), which integrates the residual block concept into a cycle-consistent GAN (cycle-GAN). In cycle-GAN, an inverse transformation was implemented between the INU uncorrected and corrected magnetic resonance imaging (MRI) images to constrain the model through forcing the calculation of both an INU corrected MRI and a synthetic corrected MRI. A fully convolution neural network integrating residual blocks was applied in the generator of cycle-GAN to enhance end-to-end raw MRI to INU corrected MRI transformation. A cohort of 55 abdominal patients with T1-weighted MR INU images and their corrections with a clinically established and commonly used method, namely, N4ITK were used as a pair to evaluate the proposed res-cycle GAN based INU correction algorithm. Quantitatively comparisons of normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC) indices, and spatial non-uniformity (SNU) were made among the proposed method and other approaches. Our res-cycle GAN based method achieved an NMAE of 0.011 ± 0.002, a PSNR of 28.0 ± 1.9 dB, an NCC of 0.970 ± 0.017, and a SNU of 0.298 ± 0.085. Our proposed method has significant improvements (p < 0.05) in NMAE, PSNR, NCC and SNU over other algorithms including conventional GAN and U-net. Once the model is well trained, our approach can automatically generate the corrected MR images in a few minutes, eliminating the need for manual setting of parameters.
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Affiliation(s)
- Xianjin Dai
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, United States of America
| | - Yang Lei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, United States of America
| | - Yingzi Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, United States of America
| | - Tonghe Wang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, United States of America
| | - Lei Ren
- Department of Radiation Oncology, Duke University, Durham, NC, 27708, United States of America
| | - Walter J Curran
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, United States of America
| | - Pretesh Patel
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, United States of America
| | - Tian Liu
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, United States of America
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, United States of America
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Isaacs BR, Keuken MC, Alkemade A, Temel Y, Bazin PL, Forstmann BU. Methodological Considerations for Neuroimaging in Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson's Disease Patients. J Clin Med 2020; 9:E3124. [PMID: 32992558 PMCID: PMC7600568 DOI: 10.3390/jcm9103124] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/17/2020] [Accepted: 09/25/2020] [Indexed: 12/17/2022] Open
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus is a neurosurgical intervention for Parkinson's disease patients who no longer appropriately respond to drug treatments. A small fraction of patients will fail to respond to DBS, develop psychiatric and cognitive side-effects, or incur surgery-related complications such as infections and hemorrhagic events. In these cases, DBS may require recalibration, reimplantation, or removal. These negative responses to treatment can partly be attributed to suboptimal pre-operative planning procedures via direct targeting through low-field and low-resolution magnetic resonance imaging (MRI). One solution for increasing the success and efficacy of DBS is to optimize preoperative planning procedures via sophisticated neuroimaging techniques such as high-resolution MRI and higher field strengths to improve visualization of DBS targets and vasculature. We discuss targeting approaches, MRI acquisition, parameters, and post-acquisition analyses. Additionally, we highlight a number of approaches including the use of ultra-high field (UHF) MRI to overcome limitations of standard settings. There is a trade-off between spatial resolution, motion artifacts, and acquisition time, which could potentially be dissolved through the use of UHF-MRI. Image registration, correction, and post-processing techniques may require combined expertise of traditional radiologists, clinicians, and fundamental researchers. The optimization of pre-operative planning with MRI can therefore be best achieved through direct collaboration between researchers and clinicians.
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Affiliation(s)
- Bethany R. Isaacs
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (A.A.); (P.-L.B.); (B.U.F.)
- Department of Experimental Neurosurgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands;
| | - Max C. Keuken
- Municipality of Amsterdam, Services & Data, Cluster Social, 1000 AE Amsterdam, The Netherlands;
| | - Anneke Alkemade
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (A.A.); (P.-L.B.); (B.U.F.)
| | - Yasin Temel
- Department of Experimental Neurosurgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands;
| | - Pierre-Louis Bazin
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (A.A.); (P.-L.B.); (B.U.F.)
- Max Planck Institute for Human Cognitive and Brain Sciences, D-04103 Leipzig, Germany
| | - Birte U. Forstmann
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (A.A.); (P.-L.B.); (B.U.F.)
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Saake M, Hepp T, Schmidle A, Wuest W, Heiss R, Doerfler A, Uder M, Bäuerle T. Influence of Artifact Corrections on MRI Signal Intensity Ratios for Assessment of Gadolinium Brain Retention. Acad Radiol 2020; 27:744-749. [PMID: 31466889 DOI: 10.1016/j.acra.2019.07.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 07/22/2019] [Accepted: 07/22/2019] [Indexed: 11/26/2022]
Abstract
RATIONALE AND OBJECTIVES Differences in brain signal intensity ratios (SIRs) of deep brain nuclei in T1-weighted (T1w) magnetic resonance images were reported as an indicator of gadolinium brain retention. Variable methods of image reconstruction and inhomogeneity correction for T1w images exist, which might affect the accuracy of SIRs. The aim of our prospective study was to investigate the effect of flow artifact compensation (FAC) and intensity inhomogeneity correction (IIC) on the dentate nucleus-to-pons and globus pallidus-to-thalamus SIRs in study participants who had previously received multiple doses of gadobutrol. MATERIALS AND METHODS This study included 76 participants who received five or more gadobutrol-enhanced scans between 2007 and 2017. A control group of 25 participants without gadolinium-based contrast agent application in their patient history was included for comparison. Unenhanced brain magnetic resonance imaging including two T1w spin-echo sequences with and without FAC was performed in all participants. Both sequences were reconstructed with and without IIC. Images were assessed for flow artifacts and SIRs were calculated. RESULTS Using FAC, a lower proportion of participants had to be excluded from the final analysis of dentate nucleus-to-pons SIR due to flow artifacts (15% versus 46%, p < 0.001). Without IIC, a difference was found between the study and the control group for the dentate nucleus-to-pons ratio (p = 0.004), but not for the same sequence reconstructed with IIC (p = 0.29). For the globus pallidus-to-thalamus ratio, no difference was found between the study and control group. CONCLUSION The application of an IIC algorithm has significant impact on brain nuclei SIRs for the assessment of gadolinium brain retention.
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El Mendili MM, Petracca M, Podranski K, Fleysher L, Cocozza S, Inglese M. SUITer: An Automated Method for Improving Segmentation of Infratentorial Structures at Ultra-High-Field MRI. J Neuroimaging 2019; 30:28-39. [PMID: 31691416 DOI: 10.1111/jon.12672] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 10/11/2019] [Accepted: 10/11/2019] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND AND PURPOSE The advent of high and ultra-high-field MRI has significantly improved the investigation of infratentorial structures by providing high-resolution images. However, none of the publicly available methods for cerebellar image analysis has been optimized for high-resolution images yet. METHODS We present the implementation of an automated algorithm-SUITer (spatially unbiased infratentorial for enhanced resolution) method for cerebellar lobules parcellation on high-resolution MR images acquired at both 3 and 7T MRI. SUITer was validated on five manually segmented data and compared with SUIT, FreeSurfer, and convolutional neural networks (CNN). SUITer was then applied to 3 and 7T MR images from 10 multiple sclerosis (MS) patients and 10 healthy controls (HCs). RESULTS The difference in volumes estimation for the cerebellar grey matter (GM), between the manual segmentation (ground truth), SUIT, CNN, and SUITer was reduced when computed by SUITer compared to SUIT (5.56 vs. 29.23 mL) and CNN (5.56 vs. 9.43 mL). FreeSurfer showed low volumes difference (3.56 mL). SUITer segmentations showed a high correlation (R2 = .91) and a high overlap with manual segmentations for cerebellar GM (83.46%). SUITer also showed low volumes difference (7.29 mL), high correlation (R2 = .99), and a high overlap (87.44%) for cerebellar GM segmentations across magnetic fields. SUITer showed similar cerebellar GM volume differences between MS patients and HC at both 3T and 7T (7.69 and 7.76 mL, respectively). CONCLUSIONS SUITer provides accurate segmentations of infratentorial structures across different resolutions and MR fields.
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Affiliation(s)
| | - Maria Petracca
- Department of Neurology, Icahn School of Medicine at Mount Sinai, NY
| | - Kornelius Podranski
- Department of Neurology, Icahn School of Medicine at Mount Sinai, NY.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Lazar Fleysher
- Department of Radiology, Icahn School of Medicine at Mount Sinai, NY
| | - Sirio Cocozza
- Department of Neurology, Icahn School of Medicine at Mount Sinai, NY.,Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Matilde Inglese
- Department of Neurology, Icahn School of Medicine at Mount Sinai, NY.,Department of Radiology, Icahn School of Medicine at Mount Sinai, NY.,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, NY.,Department of Neurology, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, (DINOGMI) University of Genova, Genoa, Italy
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12
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Maximov II, Alnæs D, Westlye LT. Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank. Hum Brain Mapp 2019; 40:4146-4162. [PMID: 31173439 PMCID: PMC6865652 DOI: 10.1002/hbm.24691] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/14/2019] [Accepted: 05/27/2019] [Indexed: 12/30/2022] Open
Abstract
Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates cross-study harmonisation and reduces pipeline-related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.
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Affiliation(s)
- Ivan I. Maximov
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dag Alnæs
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
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13
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Subudhi BN, Veerakumar T, Esakkirajan S, Ghosh A. Context Dependent Fuzzy Associated Statistical Model for Intensity Inhomogeneity Correction From Magnetic Resonance Images. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2019; 7:1800309. [PMID: 31281739 PMCID: PMC6537928 DOI: 10.1109/jtehm.2019.2898870] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/23/2018] [Accepted: 02/04/2019] [Indexed: 11/16/2022]
Abstract
In this paper, a novel context-dependent fuzzy set associated statistical model-based intensity inhomogeneity correction technique for magnetic resonance image (MRI) is proposed. The observed MRI is considered to be affected by intensity inhomogeneity and it is assumed to be a multiplicative quantity. In the proposed scheme the intensity inhomogeneity correction and MRI segmentation is considered as a combined task. The maximum a posteriori probability (MAP) estimation principle is explored to solve this problem. A fuzzy set associated Gibbs’ Markov random field (MRF) is considered to model the spatio-contextual information of an MRI. It is observed that the MAP estimate of the MRF model does not yield good results with any local searching strategy, as it gets trapped to local optimum. Hence, we have exploited the advantage of variable neighborhood searching (VNS)-based iterative global convergence criterion for MRF-MAP estimation. The effectiveness of the proposed scheme is established by testing it on different MRIs. Three performance evaluation measures are considered to evaluate the performance of the proposed scheme against existing state-of-the-art techniques. The simulation results establish the effectiveness of the proposed technique.
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Affiliation(s)
- Badri Narayan Subudhi
- 1Department of Electrical EngineeringIndian Institute of Technology JammuJammu181221India
| | - T Veerakumar
- 2Department of Electronics and Communication EngineeringNational Institute of TechnologyGoa403401India
| | - S Esakkirajan
- 3Department of Instrumentation and Control EngineeringPSG College of TechnologyCoimbatore641004India
| | - Ashish Ghosh
- 4Machine Intelligence UnitIndian Statistical InstituteKolkata700105India
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14
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Chebrolu VV, Kollasch PD, Deshpande V, Grinstead J, Howe BM, Frick MA, Fagan AJ, Benner T, Heidemann RM, Felmlee JP, Amrami KK. Uniform combined reconstruction of multichannel 7T knee MRI receive coil data without the use of a reference scan. J Magn Reson Imaging 2019; 50:1534-1544. [PMID: 30779475 DOI: 10.1002/jmri.26691] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 02/07/2019] [Accepted: 02/07/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND MR image intensity nonuniformity is often observed at 7T. Reference scans from the body coil used for uniformity correction at lower field strengths are typically not available at 7T. PURPOSE To evaluate the efficacy of a novel algorithm, Uniform Combined Reconstruction (UNICORN), to correct receive coil-induced nonuniformity in musculoskeletal 7T MRI without the use of a reference scan. STUDY TYPE Retrospective image analysis study. SUBJECTS MRI data of 20 subjects was retrospectively processed offline. Field Strength/Sequence: Knees of 20 subjects were imaged at 7T with a single-channel transmit, 28-channel phased-array receive knee coil. A turbo-spin-echo sequence was used to acquire 33 series of images. ASSESSMENT Three fellowship-trained musculoskeletal radiologists with cumulative experience of 42 years reviewed the images. The uniformity, contrast, signal-to-noise ratio (SNR), and overall image quality were evaluated for images with no postprocessing, images processed with N4 bias field correction algorithm, and the UNICORN algorithm. STATISTICAL TESTS Intraclass correlation coefficient (ICC) was used for measuring the interrater reliability. ICC and 95% confidence intervals (CIs) were calculated using the R statistical package employing a two-way mixed-effects model based on a mean rating (k = 3) for absolute agreement. The Wilcoxon signed-rank test with continuity correction was used for analyzing the overall image quality scores. RESULTS UNICORN was preferred among the three methods evaluated for uniformity in 97.9% of the pooled ratings, with excellent interrater agreement (ICC of 0.98, CI 0.97-0.99). UNICORN was also rated better than N4 for contrast and equivalent to N4 in SNR with ICCs of 0.80 (CI 0.72-0.86) and 0.67 (CI 0.54-0.77), respectively. The overall image quality scores for UNICORN were significantly higher than N4 (P < 6 × 10-13 ), with good to excellent interrater agreement (ICC 0.90, CI 0.86-0.93). DATA CONCLUSION Without the use of a reference scan, UNICORN provides better image uniformity, contrast, and overall image quality at 7T compared with the N4 bias field-correction algorithm. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1534-1544.
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Affiliation(s)
| | | | | | | | - Benjamin M Howe
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Matthew A Frick
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew J Fagan
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Joel P Felmlee
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
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15
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Ganzetti M, Liu Q, Mantini D. A Spatial Registration Toolbox for Structural MR Imaging of the Aging Brain. Neuroinformatics 2019; 16:167-179. [PMID: 29352390 DOI: 10.1007/s12021-018-9355-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
During aging the brain undergoes a series of structural changes, in size, shape as well as tissue composition. In particular, cortical atrophy and ventricular enlargement are often present in the brain of elderly individuals. This poses serious challenges in the spatial registration of structural MR images. In this study, we addressed this open issue by proposing an enhanced framework for MR registration and segmentation. Our solution was compared with other approaches based on the tools available in SPM12, a widely used software package. Performance of the different methods was assessed on 229 T1-weighted images collected in healthy individuals, with age ranging between 55 and 90 years old. Our method showed a consistent improvement as compared to other solutions, especially for subjects with enlarged lateral ventricles. It also provided a superior inter-subject alignment in cortical regions, with the most marked improvement in the frontal lobe. We conclude that our method is a valid alternative to standard approaches based on SPM12, and is particularly suitable for the processing of structural MR images of brains with cortical atrophy and ventricular enlargement. The method is integrated in our software toolbox MRTool, which is freely available to the scientific community.
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Affiliation(s)
- Marco Ganzetti
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, Leuven, Belgium.
| | - Quanying Liu
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Neural Control of Movement Lab, ETH Zurich, Zurich, Switzerland
| | - Dante Mantini
- Laboratory of Movement Control and Neuroplasticity, KU Leuven, Leuven, Belgium.,Neural Control of Movement Lab, ETH Zurich, Zurich, Switzerland.,Department of Experimental Psychology, Oxford University, Oxford, UK
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16
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Uddin MN, Figley TD, Figley CR. Effect of echo time and T2-weighting on GRASE-based T1w/T2w ratio measurements at 3T. Magn Reson Imaging 2018; 51:35-43. [DOI: 10.1016/j.mri.2018.04.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 04/17/2018] [Accepted: 04/18/2018] [Indexed: 12/24/2022]
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17
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Knight J, Taylor GW, Khademi A. Voxel-Wise Logistic Regression and Leave-One-Source-Out Cross Validation for white matter hyperintensity segmentation. Magn Reson Imaging 2018; 54:119-136. [PMID: 29932970 DOI: 10.1016/j.mri.2018.06.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 06/11/2018] [Accepted: 06/13/2018] [Indexed: 12/21/2022]
Abstract
Many algorithms have been proposed for automated segmentation of white matter hyperintensities (WMH) in brain MRI. Yet, broad uptake of any particular algorithm has not been observed. In this work, we argue that this may be due to variable and suboptimal validation data and frameworks, precluding direct comparison of methods on heterogeneous data. As a solution, we present Leave-One-Source-Out Cross Validation (LOSO-CV), which leverages all available data for performance estimation, and show that this gives more realistic (lower) estimates of segmentation algorithm performance on data from different scanners. We also develop a FLAIR-only WMH segmentation algorithm: Voxel-Wise Logistic Regression (VLR), inspired by the open-source Lesion Prediction Algorithm (LPA). Our variant facilitates more accurate parameter estimation, and permits intuitive interpretation of model parameters. We illustrate the performance of the VLR algorithm using the LOSO-CV framework with a dataset comprising freely available data from several recent competitions (96 images from 7 scanners). The performance of the VLR algorithm (median Similarity Index of 0.69) is compared to its LPA predecessor (0.58), and the results of the VLR algorithm in the 2017 WMH Segmentation Competition are also presented.
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Affiliation(s)
- Jesse Knight
- University of Guelph, 50 Stone Rd E, Guelph, Canada.
| | - Graham W Taylor
- University of Guelph, 50 Stone Rd E, Guelph, Canada; Vector Institute, 101 College Street, Toronto, Suite HL30B, Canada
| | - April Khademi
- Ryerson University, 350 Victoria St, Toronto, Canada
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18
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Lottman KK, White DM, Kraguljac NV, Reid MA, Calhoun VD, Catao F, Lahti AC. Four-way multimodal fusion of 7 T imaging data using an mCCA+jICA model in first-episode schizophrenia. Hum Brain Mapp 2018; 39:1475-1488. [PMID: 29315951 DOI: 10.1002/hbm.23906] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 11/06/2017] [Accepted: 11/26/2017] [Indexed: 01/05/2023] Open
Abstract
Acquisition of multimodal brain imaging data for the same subject has become more common leading to a growing interest in determining the intermodal relationships between imaging modalities to further elucidate the pathophysiology of schizophrenia. Multimodal data have previously been individually analyzed and subsequently integrated; however, these analysis techniques lack the ability to examine true modality inter-relationships. The utilization of a multiset canonical correlation and joint independent component analysis (mCCA + jICA) model for data fusion allows shared or distinct abnormalities between modalities to be examined. In this study, first-episode schizophrenia patients (nSZ =19) and matched controls (nHC =21) completed a resting-state functional magnetic resonance imaging (fMRI) scan at 7 T. Grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and amplitude of low frequency fluctuation (ALFF) maps were used as features in a mCCA + jICA model. Results of the mCCA + jICA model indicated three joint group-discriminating components (GM-CSF, WM-ALFF, GM-ALFF) and two modality-unique group-discriminating components (GM, WM). The joint component findings are highlighted by GM basal ganglia, somatosensory, parietal lobe, and thalamus abnormalities associated with ventricular CSF volume; WM occipital and frontal lobe abnormalities associated with temporal lobe function; and GM frontal, temporal, parietal, and occipital lobe abnormalities associated with caudate function. These results support and extend major findings throughout the literature using independent single modality analyses. The multimodal fusion of 7 T data in this study provides a more comprehensive illustration of the relationships between underlying neuronal abnormalities associated with schizophrenia than examination of imaging data independently.
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Affiliation(s)
- Kristin K Lottman
- Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, Alabama
| | - David M White
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nina V Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Meredith A Reid
- Department of Electrical and Computer Engineering, MRI Research Center, Auburn University, Auburn, Alabama
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Fabio Catao
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, Alabama
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19
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Wang Y, Chen Y, Wu D, Wang Y, Sethi SK, Yang G, Xie H, Xia S, Haacke EM. STrategically Acquired Gradient Echo (STAGE) imaging, part II: Correcting for RF inhomogeneities in estimating T1 and proton density. Magn Reson Imaging 2017; 46:140-150. [PMID: 29061370 DOI: 10.1016/j.mri.2017.10.006] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2017] [Revised: 10/16/2017] [Accepted: 10/18/2017] [Indexed: 11/19/2022]
Abstract
PURPOSE To develop a method for mapping the B1 transmit (B1t) and B1 receive (B1r) fields from two gradient echo datasets each with a different flip angle and from these two images obtain accurate T1 and proton density (PD) maps of the brain. METHODS A strategically acquired gradient echo (STAGE) data set is collected using two flip angles each with multiple echoes. The B1t field extraction was based on forcing cortical gray matter and white matter to have specific T1 values and fitting the resulting B1t field to a quadratic function. The B1r field extraction was based on synthesizing isointense images despite there being two or three tissue types present in the brain. This method was tested on 10 healthy volunteers and 20 stroke patients from data acquired at 3.0Tesla. RESULTS With the knowledge of the B1t and B1r fields, the uniformity of tissue T1 and PD maps was considerably improved. T1 values were measured for both the midbrain and basal ganglia and found to be in good agreement with the literature. DISCUSSION AND CONCLUSIONS STAGE provides a practical way to assess the B1t and the B1r fields which can then be used to correct for spatial variations in the images.
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Affiliation(s)
- Yu Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yongsheng Chen
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China; The MRI Institute for Biomedical Research, Detroit, MI, USA; Department of Radiology, School of Medicine, Wayne State University, Detroit, MI, USA
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Ying Wang
- Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA
| | - Sean K Sethi
- The MRI Institute for Biomedical Research, Detroit, MI, USA; Magnetic Resonance Innovations, Inc., Detroit, MI 48202, USA
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Haibin Xie
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Shuang Xia
- Tianjin First Central Hospital, Tianjin, China
| | - E Mark Haacke
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China; Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China; The MRI Institute for Biomedical Research, Detroit, MI, USA; Department of Radiology, School of Medicine, Wayne State University, Detroit, MI, USA; Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA; Magnetic Resonance Innovations, Inc., Detroit, MI 48202, USA.
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20
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Hagberg GE, Bause J, Ethofer T, Ehses P, Dresler T, Herbert C, Pohmann R, Shajan G, Fallgatter A, Pavlova MA, Scheffler K. Whole brain MP2RAGE-based mapping of the longitudinal relaxation time at 9.4T. Neuroimage 2017; 144:203-216. [DOI: 10.1016/j.neuroimage.2016.09.047] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Revised: 09/16/2016] [Accepted: 09/20/2016] [Indexed: 11/16/2022] Open
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21
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Arshad M, Stanley JA, Raz N. Test-retest reliability and concurrent validity of in vivo myelin content indices: Myelin water fraction and calibrated T 1 w/T 2 w image ratio. Hum Brain Mapp 2016; 38:1780-1790. [PMID: 28009069 DOI: 10.1002/hbm.23481] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Revised: 11/06/2016] [Accepted: 11/21/2016] [Indexed: 11/09/2022] Open
Abstract
In an age-heterogeneous sample of healthy adults, we examined test-retest reliability (with and without participant repositioning) of two popular MRI methods of estimating myelin content: modeling the short spin-spin (T2 ) relaxation component of multi-echo imaging data and computing the ratio of T1 -weighted and T2 -weighted images (T1 w/T2 w). Taking the myelin water fraction (MWF) index of myelin content derived from the multi-component T2 relaxation data as a standard, we evaluate the concurrent and differential validity of T1 w/T2 w ratio images. The results revealed high reliability of MWF and T1 w/T2 w ratio. However, we found significant correlations of low to moderate magnitude between MWF and the T1 w/T2 w ratio in only two of six examined regions of the cerebral white matter. Notably, significant correlations of the same or greater magnitude were observed for T1 w/T2 w ratio and the intermediate T2 relaxation time constant, which is believed to reflect differences in the mobility of water between the intracellular and extracellular compartments. We conclude that although both methods are highly reliable and thus well-suited for longitudinal studies, T1 w/T2 w ratio has low criterion validity and may be not an optimal index of subcortical myelin content. Hum Brain Mapp 38:1780-1790, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Muzamil Arshad
- Department of Psychiatry and Behavioral Neuroscience, School of Medicine, Wayne State University, Detroit, Michigan.,Institute of Gerontology, Wayne State University, Detroit, Michigan
| | - Jeffrey A Stanley
- Department of Psychiatry and Behavioral Neuroscience, School of Medicine, Wayne State University, Detroit, Michigan
| | - Naftali Raz
- Institute of Gerontology, Wayne State University, Detroit, Michigan.,Department of Psychology, Wayne State University, Detroit, Michigan
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22
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Ganzetti M, Wenderoth N, Mantini D. Intensity Inhomogeneity Correction of Structural MR Images: A Data-Driven Approach to Define Input Algorithm Parameters. Front Neuroinform 2016; 10:10. [PMID: 27014050 PMCID: PMC4791378 DOI: 10.3389/fninf.2016.00010] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2015] [Accepted: 02/26/2016] [Indexed: 12/03/2022] Open
Abstract
Intensity non-uniformity (INU) in magnetic resonance (MR) imaging is a major issue when conducting analyses of brain structural properties. An inaccurate INU correction may result in qualitative and quantitative misinterpretations. Several INU correction methods exist, whose performance largely depend on the specific parameter settings that need to be chosen by the user. Here we addressed the question of how to select the best input parameters for a specific INU correction algorithm. Our investigation was based on the INU correction algorithm implemented in SPM, but this can be in principle extended to any other algorithm requiring the selection of input parameters. We conducted a comprehensive comparison of indirect metrics for the assessment of INU correction performance, namely the coefficient of variation of white matter (CVWM), the coefficient of variation of gray matter (CVGM), and the coefficient of joint variation between white matter and gray matter (CJV). Using simulated MR data, we observed the CJV to be more accurate than CVWM and CVGM, provided that the noise level in the INU-corrected image was controlled by means of spatial smoothing. Based on the CJV, we developed a data-driven approach for selecting INU correction parameters, which could effectively work on actual MR images. To this end, we implemented an enhanced procedure for the definition of white and gray matter masks, based on which the CJV was calculated. Our approach was validated using actual T1-weighted images collected with 1.5 T, 3 T, and 7 T MR scanners. We found that our procedure can reliably assist the selection of valid INU correction algorithm parameters, thereby contributing to an enhanced inhomogeneity correction in MR images.
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Affiliation(s)
- Marco Ganzetti
- Neural Control of Movement Laboratory, ETH ZurichZurich, Switzerland
- Department of Experimental Psychology, University of OxfordOxford, UK
| | - Nicole Wenderoth
- Neural Control of Movement Laboratory, ETH ZurichZurich, Switzerland
| | - Dante Mantini
- Neural Control of Movement Laboratory, ETH ZurichZurich, Switzerland
- Department of Experimental Psychology, University of OxfordOxford, UK
- Laboratory of Movement Control and Neuroplasticity, Katholieke Universiteit LeuvenLeuven, Belgium
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